Loss Function

Loss functions are crucial components of machine learning models, guiding the learning process by quantifying the difference between predicted and actual values. Current research emphasizes developing loss functions tailored to specific challenges, such as class imbalance in classification (addressed through asymmetric losses and hyperparameter distributions) and robustness to noise and outliers (using bounded and smooth alternatives to standard functions like mean squared error). These advancements improve model accuracy, efficiency, and generalizability across diverse applications, including medical image analysis, time series prediction, and physics-informed neural networks. The ongoing exploration of loss function design directly impacts the performance and reliability of machine learning models in various scientific and engineering domains.